{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "def download(name, model, iters):\n",
    "    trained_policy = '{}-{}.iter={}.npz'.format(name, model, iters)\n",
    "    if not os.path.exists('.policy/{}'.format(trained_policy)):\n",
    "        print 'download the model from HPC server'\n",
    "        os.system('scp mercer:/scratch/jg5223/exp/.policy/{} .policy/'.format(trained_policy))\n",
    "    print 'done'\n",
    "# m ='model_wmt15_bpe2k_uni_en-ru_rev.npz'    \n",
    "# download('160920-225547', m, '44400')\n",
    "# download('160920-225339', m, '33600') \n",
    "# download('160920-230514', m, '95600')\n",
    "# download('160920-225917', m, '68000')\n",
    "# download('160920-230503', m, '48200')\n",
    "# download('160920-225454', m, '45200')\n",
    "\n",
    "# m ='model_wmt15_bpe2k_uni_en-de_rev.npz' \n",
    "# download('160925-000643', m, '24400')\n",
    "# download('160925-000657', m, '10400')\n",
    "# download('160925-000642', m, '8000') \n",
    "# download('160925-000735', m, '22200')\n",
    "# download('160925-000751', m, '26200')\n",
    "# download('160925-000811', m, '18800')\n",
    "\n",
    "# max model 172 160917-175503 Iter=34400 AP=0.3 0.14121125914\n",
    "# max model 262 160917-001117 Iter=52400 AP=0.5 0.157379802964\n",
    "# max model 141 160917-175423 Iter=28200 AP=0.7 0.15857511208\n",
    "# max model 67 160917-175657 Iter=13400 CW=5.0 0.160038686998\n",
    "m ='model_wmt15_bpe2k_uni_en-de.npz' \n",
    "download('160917-175503', m, '34400')\n",
    "download('160917-001117', m, '52400')\n",
    "download('160917-175423', m, '28200') \n",
    "download('160917-175657', m, '13400')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 3: GeForce GTX TITAN X (CNMeM is disabled, cuDNN 5005)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge configuration into options\n",
      "load options...\n",
      "Rtype: 10\n",
      "act_mask: True\n",
      "alpha_c: 0.0\n",
      "batch_size: 64\n",
      "batchsize: 10\n",
      "clip_c: 1.0\n",
      "coverage: False\n",
      "datasets: ['/misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/un16/train.un16.en-zh.en.c0.tok.clean.bpe20k.np', '/misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/un16/train.un16.en-zh.zh.c0.tok.clean.bpe20k.np']\n",
      "decay_c: 0.0\n",
      "decoder: gru_cond\n",
      "dictionaries: ['/misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/un16/train.un16.en-zh.en.c0.tok.clean.bpe20k.vocab.pkl', '/misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/un16/train.un16.en-zh.zh.c0.tok.clean.bpe20k.vocab.pkl']\n",
      "dim: 1028\n",
      "dim_word: 512\n",
      "dispFreq: 50\n",
      "encoder: gru\n",
      "finetune: True\n",
      "finish_after: 10000000\n",
      "forget: False\n",
      "full_att: False\n",
      "gamma: 1\n",
      "layernorm: False\n",
      "lr_model: 2e-05\n",
      "lr_policy: 0.0002\n",
      "lrate: 0.0001\n",
      "max_epochs: 5000\n",
      "maxlen: 50\n",
      "maxsrc: 10\n",
      "model: /misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/.pretrained/model_un16_bpe2k_uni_en-zh.npz\n",
      "n_words: 20000\n",
      "n_words_src: 20000\n",
      "optimizer: adadelta\n",
      "option: /misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/.pretrained/model_un16_bpe2k_uni_en-zh.npz.pkl\n",
      "overwrite: False\n",
      "patience: 1000\n",
      "peek: 1\n",
      "pre: False\n",
      "prop: 0.5\n",
      "recurrent: True\n",
      "reload_: False\n",
      "rl_maxlen: 100\n",
      "s0: 1\n",
      "sample: 10\n",
      "sampleFreq: 99\n",
      "saveFreq: 1000\n",
      "saveto: .pretraining/model_un16_bpe2k_uni_en-zh.npz\n",
      "step: 1\n",
      "target_ap: 0.8\n",
      "target_cw: 8\n",
      "updater: REINFORCE\n",
      "upper: False\n",
      "use_dropout: False\n",
      "validFreq: 1000\n",
      "valid_batch_size: 64\n",
      "valid_datasets: ['/misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/un16/devset.un16.en-zh.en.c0.tok.bpe20k.np', '/misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/un16/devset.un16.en-zh.zh.c0.tok.bpe20k.np']\n",
      "workspace: /misc/kcgscratch1/ChoGroup/thoma_data/simul_trans/\n",
      "loading Wemb: (20000, 512)\n",
      "loading Wemb_dec: (20000, 512)\n",
      "loading encoder_W: (512, 2056)\n",
      "loading encoder_b: (2056,)\n",
      "loading encoder_U: (1028, 2056)\n",
      "loading encoder_Wx: (512, 1028)\n",
      "loading encoder_bx: (1028,)\n",
      "loading encoder_Ux: (1028, 1028)\n",
      "loading ff_state_W: (1028, 1028)\n",
      "loading ff_state_b: (1028,)\n",
      "loading decoder_W: (512, 2056)\n",
      "loading decoder_b: (2056,)\n",
      "loading decoder_U: (1028, 2056)\n",
      "loading decoder_Wx: (512, 1028)\n",
      "loading decoder_Ux: (1028, 1028)\n",
      "loading decoder_bx: (1028,)\n",
      "loading decoder_U_nl: (1028, 2056)\n",
      "loading decoder_b_nl: (2056,)\n",
      "loading decoder_Ux_nl: (1028, 1028)\n",
      "loading decoder_bx_nl: (1028,)\n",
      "loading decoder_Wc: (1028, 2056)\n",
      "loading decoder_Wcx: (1028, 1028)\n",
      "loading decoder_W_comb_att: (1028, 1028)\n",
      "loading decoder_Wc_att: (1028, 1028)\n",
      "loading decoder_b_att: (1028,)\n",
      "loading decoder_U_att: (1028, 1)\n",
      "loading decoder_c_tt: (1,)\n",
      "loading ff_logit_lstm_W: (1028, 512)\n",
      "loading ff_logit_lstm_b: (512,)\n",
      "loading ff_logit_prev_W: (512, 512)\n",
      "loading ff_logit_prev_b: (512,)\n",
      "loading ff_logit_ctx_W: (1028, 512)\n",
      "loading ff_logit_ctx_b: (512,)\n",
      "loading ff_logit_W: (512, 20000)\n",
      "loading ff_logit_b: (20000,)\n",
      "done\n",
      "Building f_ctx/init... Done.\n",
      "Building f_sim_next.. with normal input\n",
      "Done.\n",
      "compile the initializer\n",
      "encoder done.\n",
      "build REINFORCE optimizer for the whole NMT model:\n",
      "build optimizer with Adam\n",
      "done.\n",
      "parameter initialization\n",
      "building a recurrent controller\n",
      "start from a new model: 170309-053853\n",
      "build action sampling function [Discrete]\n",
      "build action distribiution\n",
      "setup the advantages & baseline network\n",
      "build advantages and baseline gradient\n",
      "build optimizer with Adam\n",
      "build RENIFROCE.\n",
      "build REINFORCE optimizer\n",
      "build optimizer with Adam\n",
      "done\n",
      "policy network\n",
      "policy_net_in_W (2568, 256)\n",
      "policy_net_in_b (256,)\n",
      "policy_net_in_U (128, 256)\n",
      "policy_net_in_Wx (2568, 128)\n",
      "policy_net_in_bx (128,)\n",
      "policy_net_in_Ux (128, 128)\n",
      "policy_net_out_W (128, 2)\n",
      "policy_net_out_b (2,)\n",
      "scan the dataset.\n",
      "scanned 12394614 lines\n",
      "scan the dataset.\n",
      "scanned 3999 lines\n",
      "training set 12394614 lines / validation set 3999 lines\n",
      "use the reward function K\n"
     ]
    }
   ],
   "source": [
    "# %load simultrans_train.py\n",
    "\"\"\"\n",
    "Simultaneous Machine Translateion: Training with Policy Gradient\n",
    "\n",
    "\"\"\"\n",
    "import argparse\n",
    "import os\n",
    "import cPickle as pkl\n",
    "\n",
    "from bleu import *\n",
    "from nmt_uni import *\n",
    "from policy import Controller as Policy\n",
    "from utils import Progbar, Monitor\n",
    "from data_iterator import check_length, iterate\n",
    "\n",
    "from simultrans_model_clean import simultaneous_decoding\n",
    "from simultrans_model_clean import _seqs2words, _bpe2words, _padding\n",
    "from config import rl_config\n",
    "    \n",
    "import time\n",
    "\n",
    "numpy.random.seed(19920206)\n",
    "timer = time.time\n",
    "\n",
    "\n",
    "config       = rl_config()\n",
    "options_file = config['option']\n",
    "model        = config['model']\n",
    "WORK         = config['workspace']\n",
    "id, remote   = None, False\n",
    "\n",
    "# check hidden folders\n",
    "paths = ['.policy', '.pretrained', '.log', '.config', '.images', '.translate']\n",
    "for p in paths:\n",
    "    p = WORK + p\n",
    "    if not os.path.exists(p):\n",
    "        os.mkdir(p)\n",
    "\n",
    "if id is not None:\n",
    "    fcon = WORK + '.config/{}.conf'.format(id)\n",
    "    if os.path.exists(fcon):\n",
    "        print 'load config files'\n",
    "        policy, config = pkl.load(open(fcon, 'r'))\n",
    "\n",
    "# ============================================================================== #\n",
    "# load model model_options\n",
    "# ============================================================================== #\n",
    "_model = model.split('/')[-1]\n",
    "\n",
    "if options_file is not None:\n",
    "    with open(options_file, 'rb') as f:\n",
    "        options = pkl.load(f)\n",
    "else:\n",
    "    with open('%s.pkl' % model, 'rb') as f:\n",
    "        options = pkl.load(f)\n",
    "\n",
    "print 'merge configuration into options'\n",
    "for w in config:\n",
    "    # if (w in options) and (config[w] is not None):\n",
    "    options[w] = config[w]\n",
    "\n",
    "print 'load options...'\n",
    "for w, p in sorted(options.items(), key=lambda x: x[0]):\n",
    "    print '{}: {}'.format(w, p)\n",
    "\n",
    "# load detail settings from option file:\n",
    "dictionary, dictionary_target = options['dictionaries']\n",
    "\n",
    "# load source dictionary and invert\n",
    "with open(dictionary, 'rb') as f:\n",
    "    word_dict = pkl.load(f)\n",
    "word_idict = dict()\n",
    "for kk, vv in word_dict.iteritems():\n",
    "    word_idict[vv] = kk\n",
    "word_idict[0] = '<eos>'\n",
    "word_idict[1] = 'UNK'\n",
    "\n",
    "# load target dictionary and invert\n",
    "with open(dictionary_target, 'rb') as f:\n",
    "    word_dict_trg = pkl.load(f)\n",
    "word_idict_trg = dict()\n",
    "for kk, vv in word_dict_trg.iteritems():\n",
    "    word_idict_trg[vv] = kk\n",
    "word_idict_trg[0] = '<eos>'\n",
    "word_idict_trg[1] = 'UNK'\n",
    "\n",
    "options['pre'] = config['pre']\n",
    "\n",
    "# ========================================================================= #\n",
    "# Build a Simultaneous Translator\n",
    "# ========================================================================= #\n",
    "\n",
    "# allocate model parameters\n",
    "params = init_params(options)\n",
    "params = load_params(model, params)\n",
    "tparams = init_tparams(params)\n",
    "\n",
    "# print 'build the model for computing cost (full source sentence).'\n",
    "trng, use_noise, \\\n",
    "_x, _x_mask, _y, _y_mask, \\\n",
    "opt_ret, \\\n",
    "cost, f_cost = build_model(tparams, options)\n",
    "print 'done'\n",
    "\n",
    "# functions for sampler\n",
    "f_sim_ctx, f_sim_init, f_sim_next = build_simultaneous_sampler(tparams, options, trng)\n",
    "\n",
    "# function for finetune the underlying model\n",
    "if options['finetune']:\n",
    "    ff_init, ff_cost, ff_update = build_simultaneous_model(tparams, options, rl=True)\n",
    "    funcs = [f_sim_ctx, f_sim_init, f_sim_next, f_cost, ff_init, ff_cost, ff_update]\n",
    "\n",
    "else:\n",
    "    funcs = [f_sim_ctx, f_sim_init, f_sim_next, f_cost]\n",
    "\n",
    "\n",
    "# check the ID:\n",
    "options['base'] = _model\n",
    "agent     = Policy(trng, options,\n",
    "                   n_in=options['readout_dim'] + 1 if options['coverage'] else options['readout_dim'],\n",
    "                   n_out=3 if config['forget'] else 2,\n",
    "                   recurrent=options['recurrent'], id=id)\n",
    "\n",
    "# make the dataset ready for training & validation\n",
    "trainIter = TextIterator(options['datasets'][0], options['datasets'][1],\n",
    "                         options['dictionaries'][0], options['dictionaries'][1],\n",
    "                         n_words_source=options['n_words_src'], n_words_target=options['n_words'],\n",
    "                         batch_size=config['batchsize'],\n",
    "                         maxlen=options['maxlen'])\n",
    "\n",
    "train_num = trainIter.num\n",
    "\n",
    "validIter = TextIterator(options['valid_datasets'][0], options['valid_datasets'][1],\n",
    "                         options['dictionaries'][0], options['dictionaries'][1],\n",
    "                         n_words_source=options['n_words_src'], n_words_target=options['n_words'],\n",
    "                         batch_size=20, cache=10,\n",
    "                         maxlen=1000000)\n",
    "\n",
    "valid_num = validIter.num\n",
    "print 'training set {} lines / validation set {} lines'.format(train_num, valid_num)\n",
    "print 'use the reward function {}'.format(chr(config['Rtype'] + 65))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Simultaneous Translator...\n"
     ]
    }
   ],
   "source": [
    "# ================================================================================= #\n",
    "# Main Loop: Run\n",
    "# ================================================================================= #\n",
    "print 'Start Simultaneous Translator...'\n",
    "monitor = None\n",
    "if remote:\n",
    "    monitor = Monitor(root='http://localhost:9000')\n",
    "\n",
    "# freqs\n",
    "save_freq     = 200\n",
    "sample_freq   = 10\n",
    "valid_freq    = 200\n",
    "valid_size    = 200\n",
    "display_freq  = 50\n",
    "finetune_freq = 5\n",
    "\n",
    "history, last_it = agent.load()\n",
    "action_space = ['W', 'C', 'F']\n",
    "Log_avg = {}\n",
    "time0 = timer()\n",
    "\n",
    "pipe = OrderedDict()\n",
    "for key in ['x', 'x_mask', 'y', 'y_mask', 'c_mask']:\n",
    "    pipe[key] = []\n",
    "\n",
    "def _translate(src, trg, samples=None, train=False,\n",
    "               greedy=False, show=False, full=False):\n",
    "    time0 = time.time()\n",
    "    if full:\n",
    "        options1 = copy.copy(options)\n",
    "        options1['finetune'] = False\n",
    "    else:\n",
    "        options1 = options\n",
    "\n",
    "    ret   = simultaneous_decoding(\n",
    "            funcs, agent, options1,\n",
    "            src, trg, word_idict_trg,\n",
    "            samples, greedy, train)\n",
    "\n",
    "    if show:\n",
    "        info   = ret[1]\n",
    "        values = [(w, float(info[w])) for w in info if w != 'advantages']\n",
    "        print ' , '.join(['{}={:.3f}'.format(k, f) for k, f in values]),\n",
    "        print '...{}s'.format(time.time() - time0)\n",
    "\n",
    "    return ret\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "srcs, trgs = trainIter.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43 60 44 42 47 44 51 43 41 45\n"
     ]
    }
   ],
   "source": [
    "for s in srcs:\n",
    "    print len(s),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adv=0.197 , B_loss=0.014 , p(COMMIT)=0.454 , p(WAIT)=0.546 , StartR=0.061 , J=-0.111 , Delay=0.611 , a_cost=-4.762 , Entropy=-0.027 , Quality=0.034 ...7.75953602791s\n"
     ]
    }
   ],
   "source": [
    "# training set sentence tuning\n",
    "new_srcs, new_trgs = [], []\n",
    "for src, trg in zip(srcs, trgs):\n",
    "    if len(src) <= options['s0']:\n",
    "        continue  # ignore when the source sentence is less than sidx.\n",
    "    else:\n",
    "        new_srcs += [src]\n",
    "        new_trgs += [trg]\n",
    "\n",
    "srcs, trgs = new_srcs, new_trgs\n",
    "statistics, info = _translate(srcs, trgs, train=True, show=True, greedy=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3: (61,) 2: (61,) 2: (61,) 3: (61,) 3: (61,) 4: (61,) 4: (61,) 3: (61,) 4: (61,) 6: (61,) 5: (61,) 5: (61,) 5: (61,) 5: (61,) 9: (61,) 7: (61,) 6: (61,) 7: (61,) 8: (61,) 9: (61,) 11: (61,) 13: (61,) 10: (61,) 10: (61,) 11: (61,) 10: (61,) 8: (61,) 9: (61,) 8: (61,) 10: (61,) 15: (61,) 13: (61,) 14: (61,) 18: (61,) 13: (61,) 15: (61,) 13: (61,) 13: (61,) 15: (61,) 11: (61,) 16: (61,) 14: (61,) 15: (61,) 11: (61,) 16: (61,) 12: (61,) 16: (61,) 12: (61,) 19: (61,) 18: (61,) 13: (61,) 17: (61,) 15: (61,) 19: (61,) 18: (61,) 14: (61,) 16: (61,) 17: (61,) 15: (61,) 18: (61,) 18: (61,) 16: (61,) 17: (61,) 24: (61,) 20: (61,) 19: (61,) 23: (61,) 19: (61,) 19: (61,) 22: (61,) 22: (61,) 18: (61,) 18: (61,) 27: (61,) 30: (61,) 24: (61,) 27: (61,) 18: (61,) 26: (61,) 24: (61,) 29: (61,) 28: (61,) 26: (61,) 32: (61,) 25: (61,) 39: (61,) 34: (61,) 29: (61,) 32: (61,) 33: (61,) 33: (61,) 26: (61,) 37: (61,) 40: (61,) 37: (61,) 44: (61,) 50: (61,) 60: (61,) 59: (61,) 70: (61,)\n"
     ]
    }
   ],
   "source": [
    "for a in statistics['attentions']:\n",
    "    print '{}: {}'.format(len(a), a[0].shape),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Id: 20\n",
      "source:  \u001b[36mHe\u001b[0m \u001b[36mnoted\u001b[0m \u001b[36mthat\u001b[0m \u001b[32mmost\u001b[0m \u001b[32mof\u001b[0m \u001b[33mthe\u001b[0m \u001b[31mparticipants\u001b[0m \u001b[37min\u001b[0m \u001b[37mthe\u001b[0m \u001b[37msymposium\u001b[0m \u001b[37mwere\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mview\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mhost\u001b[0m \u001b[37mcountries\u001b[0m \u001b[37mshould\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mable\u001b[0m \u001b[37mto\u001b[0m \u001b[37mregulate\u001b[0m \u001b[37mFDI\u001b[0m \u001b[37mso\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mthey\u001b[0m \u001b[37mcould\u001b[0m \u001b[37mchoose\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mown\u001b[0m \u001b[37mparticular\u001b[0m \u001b[37mmix\u001b[0m \u001b[37mof\u001b[0m \u001b[37mpolicies\u001b[0m \u001b[37mand\u001b[0m \u001b[37mconditions\u001b[0m \u001b[37mrelating\u001b[0m \u001b[37mto\u001b[0m \u001b[37mFDI\u001b[0m \u001b[37mkeeping\u001b[0m \u001b[37min\u001b[0m \u001b[37mmind\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mdevelopmental\u001b[0m \u001b[37mneeds\u001b[0m\n",
      "sample:  \u001b[36m他\u001b[0m \u001b[36m指出\u001b[0m \u001b[32m大多数\u001b[0m \u001b[32m成员\u001b[0m \u001b[32m的\u001b[0m \u001b[32m作者\u001b[0m \u001b[32m多半\u001b[0m \u001b[33m都\u001b[0m \u001b[33m是\u001b[0m \u001b[31m参与者\u001b[0m\n",
      "source:  He noted that most of the participants in the symposium were of the view that host countries should be able to regulate FDI so that they could choose their own particular mix of policies and conditions relating to FDI keeping in mind their developmental needs\n",
      "sample:  他 指出 大多数 成员 的 作者 多半 都 是 参与者\n",
      "target:  他 指出 大多数 与会者 认为 接受 投资 的 国家 应该 能够 管理 外国 直接 投资 以便 能 结合 自己 的 发展 需要 选择 有 自己 特点 的 外国 直接 投资 政策 和 条件 的 组合\n",
      "quality: 0.0013957233579\n",
      "delay: 0.740259730646\n",
      "reward: 0.0027914467158\n",
      "--Id: 21\n",
      "source:  \u001b[36mThe\u001b[0m \u001b[36mSpecial\u001b[0m \u001b[32mRepresentative\u001b[0m \u001b[33mnoted\u001b[0m \u001b[31mthat\u001b[0m \u001b[37mthe\u001b[0m \u001b[37muse\u001b[0m \u001b[37mof\u001b[0m \u001b[37mcriminal\u001b[0m \u001b[37mcharges\u001b[0m \u001b[37msuch\u001b[0m \u001b[37mas\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mencouraging\u001b[0m \u001b[37mhatred\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mState\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mand\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mdistributing\u001b[0m \u001b[37mfal@@\u001b[0m \u001b[37mse@@\u001b[0m \u001b[37mhoods\u001b[0m \u001b[37mand\u001b[0m \u001b[37mr@@\u001b[0m \u001b[37mum@@\u001b[0m \u001b[37mors\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mfrequently\u001b[0m \u001b[37mimplies\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mrisk\u001b[0m \u001b[37mof\u001b[0m \u001b[37msup@@\u001b[0m \u001b[37mpressing\u001b[0m \u001b[37mlegitimate\u001b[0m \u001b[37mfree\u001b[0m \u001b[37mspeech\u001b[0m \u001b[37mand\u001b[0m \u001b[37mis\u001b[0m \u001b[37mparticularly\u001b[0m \u001b[37mworrying\u001b[0m \u001b[37mwhen\u001b[0m \u001b[37msuch\u001b[0m \u001b[37mcharges\u001b[0m \u001b[37mare\u001b[0m \u001b[37mraised\u001b[0m \u001b[37magainst\u001b[0m \u001b[37ma\u001b[0m \u001b[37mperson\u001b[0m \u001b[37mfor\u001b[0m \u001b[37mhaving\u001b[0m \u001b[37mden@@\u001b[0m \u001b[37mounced\u001b[0m \u001b[37malleged\u001b[0m \u001b[37mhuman\u001b[0m \u001b[37mrights\u001b[0m \u001b[37mviolations\u001b[0m\n",
      "sample:  \u001b[36m新@@\u001b[0m \u001b[36m纪@@\u001b[0m \u001b[36m元\u001b[0m \u001b[36m权利\u001b[0m \u001b[32m特别\u001b[0m \u001b[32m代表\u001b[0m \u001b[32m支持\u001b[0m \u001b[33m反\u001b[0m \u001b[33m性别歧视\u001b[0m \u001b[31m组织\u001b[0m \u001b[31m的\u001b[0m \u001b[31m代表\u001b[0m\n",
      "source:  The Special Representative noted that the use of criminal charges such as UNK encouraging hatred of the State UNK and UNK distributing fal@@ se@@ hoods and r@@ um@@ ors UNK frequently implies the risk of sup@@ pressing legitimate free speech and is particularly worrying when such charges are raised against a person for having den@@ ounced alleged human rights violations\n",
      "sample:  新@@ 纪@@ 元 权利 特别 代表 支持 反 性别歧视 组织 的 代表\n",
      "target:  35 特别 代表 注意 到 采用 诸如 鼓励 对 国家 的 仇恨 和 散布 谎言 和 UNK 言 等 指控 常常 意味着 压制 合法 自由言论 的 风险 在 针对 谴责 指称 侵犯 人权 事项 者 提出 这种 指控 时 尤其 令人担心\n",
      "quality: 0.000254578728715\n",
      "delay: 0.692307681657\n",
      "reward: 0.00050915745743\n",
      "--Id: 22\n",
      "source:  \u001b[36m178\u001b[0m \u001b[36mThe\u001b[0m \u001b[36mWorking\u001b[0m \u001b[36mGroup\u001b[0m \u001b[36mwishes\u001b[0m \u001b[32mto\u001b[0m \u001b[33mremind\u001b[0m \u001b[31mthe\u001b[0m \u001b[35mGovernment\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mRepublic\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mCongo\u001b[0m \u001b[37mof\u001b[0m \u001b[37mits\u001b[0m \u001b[37mresponsibility\u001b[0m \u001b[37mto\u001b[0m \u001b[37mconduct\u001b[0m \u001b[37mthorough\u001b[0m \u001b[37mand\u001b[0m \u001b[37mimpartial\u001b[0m \u001b[37minvestigations\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mfor\u001b[0m \u001b[37mas\u001b[0m \u001b[37mlong\u001b[0m \u001b[37mas\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mfate\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mvictim\u001b[0m \u001b[37mof\u001b[0m \u001b[37menforced\u001b[0m \u001b[37mdisappearance\u001b[0m \u001b[37mremains\u001b[0m \u001b[37mun@@\u001b[0m \u001b[37mclarified\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37min\u001b[0m \u001b[37maccordance\u001b[0m \u001b[37mwith\u001b[0m \u001b[37marticle\u001b[0m \u001b[37m13\u001b[0m \u001b[37mparagraph\u001b[0m \u001b[37m6\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mDeclaration\u001b[0m\n",
      "sample:  \u001b[36m178\u001b[0m \u001b[36m工作组\u001b[0m \u001b[36m希望\u001b[0m \u001b[36m工作组\u001b[0m \u001b[32m还\u001b[0m \u001b[32m祝愿\u001b[0m \u001b[33m工作组\u001b[0m \u001b[31m进一步\u001b[0m \u001b[35mUNK\u001b[0m\n",
      "source:  178 The Working Group wishes to remind the Government of the Republic of the Congo of its responsibility to conduct thorough and impartial investigations UNK for as long as the fate of the victim of enforced disappearance remains un@@ clarified UNK in accordance with article 13 paragraph 6 of the Declaration\n",
      "sample:  178 工作组 希望 工作组 还 祝愿 工作组 进一步 UNK\n",
      "target:  178 工作组 谨 提请 刚果共和国 政府 注意 其所 承担 的 责任 即 按照 宣言 第 13 条 第 6 款 进行 彻底 和 公正 的 调查 直至 查明 被 强迫 失踪 的 人 的 命运 为止\n",
      "quality: 0.000293064431893\n",
      "delay: 0.722222214198\n",
      "reward: 0.000586128863786\n",
      "--Id: 23\n",
      "source:  \u001b[36mIn\u001b[0m \u001b[32minformation\u001b[0m \u001b[33mprovided\u001b[0m \u001b[33mon\u001b[0m \u001b[33mthe\u001b[0m \u001b[31mapplication\u001b[0m \u001b[31mof\u001b[0m \u001b[31mthe\u001b[0m \u001b[31mCartagena\u001b[0m \u001b[37mAction\u001b[0m \u001b[37mPlan\u001b[0m \u001b[37mon\u001b[0m \u001b[37m13\u001b[0m \u001b[37mSeptember\u001b[0m \u001b[37m2010\u001b[0m \u001b[37mEthiopia\u001b[0m \u001b[37mreported\u001b[0m \u001b[37m13\u001b[0m \u001b[37mareas\u001b[0m \u001b[37min\u001b[0m \u001b[37mwhich\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mmines\u001b[0m \u001b[37mare\u001b[0m \u001b[37mknown\u001b[0m \u001b[37mto\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mem@@\u001b[0m \u001b[37mplaced\u001b[0m \u001b[37mand\u001b[0m \u001b[37m44\u001b[0m \u001b[37mareas\u001b[0m \u001b[37min\u001b[0m \u001b[37mwhich\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mmines\u001b[0m \u001b[37mare\u001b[0m \u001b[37msuspected\u001b[0m \u001b[37mto\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mem@@\u001b[0m \u001b[37mplaced\u001b[0m\n",
      "sample:  \u001b[36m并且\u001b[0m \u001b[36m涉及\u001b[0m \u001b[32m资料\u001b[0m \u001b[33m中\u001b[0m \u001b[31m关于\u001b[0m \u001b[31m适用\u001b[0m \u001b[31m卡塔赫纳\u001b[0m \u001b[31m土地\u001b[0m \u001b[31m产品\u001b[0m\n",
      "source:  In information provided on the application of the Cartagena Action Plan on 13 September 2010 Ethiopia reported 13 areas in which UNK mines are known to be em@@ placed and 44 areas in which UNK mines are suspected to be em@@ placed\n",
      "sample:  并且 涉及 资料 中 关于 适用 卡塔赫纳 土地 产品\n",
      "target:  埃塞俄比亚 在 2010 年 9 月 13 日 关于 实施 卡塔赫纳 行动计划 的 资料 中 报告 了 13 个 已知 布设 了 杀伤 人员 地雷 的 区域 和 44 个 怀疑 布设 了 杀伤 人员 地雷 的 区域\n",
      "quality: 0.000233381292852\n",
      "delay: 0.699999992222\n",
      "reward: 0.000466762585704\n",
      "--Id: 24\n",
      "source:  \u001b[36mDirec@@\u001b[0m \u001b[32mting\u001b[0m \u001b[32mattacks\u001b[0m \u001b[32magainst\u001b[0m \u001b[33mpersonnel\u001b[0m \u001b[31minstallations\u001b[0m \u001b[35mmaterial\u001b[0m \u001b[36munits\u001b[0m \u001b[32mor\u001b[0m \u001b[37mvehicles\u001b[0m \u001b[37minvolved\u001b[0m \u001b[37min\u001b[0m \u001b[37ma\u001b[0m \u001b[37mhumanitarian\u001b[0m \u001b[37massistance\u001b[0m \u001b[37mor\u001b[0m \u001b[37mpeacekeeping\u001b[0m \u001b[37mmission\u001b[0m \u001b[37min\u001b[0m \u001b[37maccordance\u001b[0m \u001b[37mwith\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mUnited\u001b[0m \u001b[37mNations\u001b[0m \u001b[37mCharter\u001b[0m \u001b[37mas\u001b[0m \u001b[37mlong\u001b[0m \u001b[37mas\u001b[0m \u001b[37mthey\u001b[0m \u001b[37mare\u001b[0m \u001b[37mentitled\u001b[0m \u001b[37mto\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mprotection\u001b[0m \u001b[37mgiven\u001b[0m \u001b[37mto\u001b[0m \u001b[37mcivilians\u001b[0m \u001b[37mor\u001b[0m \u001b[37mcivilian\u001b[0m \u001b[37mobjects\u001b[0m \u001b[37munder\u001b[0m \u001b[37minternational\u001b[0m \u001b[37mhumanitarian\u001b[0m \u001b[37mlaw\u001b[0m\n",
      "sample:  \u001b[36m方向\u001b[0m \u001b[36m指示\u001b[0m \u001b[32m性\u001b[0m \u001b[33m打击\u001b[0m \u001b[31m攻击\u001b[0m \u001b[35m办法\u001b[0m \u001b[36m物资\u001b[0m \u001b[36m单位\u001b[0m \u001b[32m或\u001b[0m \u001b[32m航线\u001b[0m\n",
      "source:  Direc@@ ting attacks against personnel installations material units or vehicles involved in a humanitarian assistance or peacekeeping mission in accordance with the United Nations Charter as long as they are entitled to the protection given to civilians or civilian objects under international humanitarian law\n",
      "sample:  方向 指示 性 打击 攻击 办法 物资 单位 或 航线\n",
      "target:  UNK 攻击 执行 人道主义 援助 行动 的 人员 设施 物资 单位 或 车辆 或 根据 联合国 宪章 设立 的 维持 和平 特派团 只要 后者 根据 国际 人道主义法 有权 享有 相同 于 平民 或 民用 物体 的 保护\n",
      "quality: 0.00093558134552\n",
      "delay: 0.676767669932\n",
      "reward: 0.00187116269104\n",
      "--Id: 25\n",
      "source:  \u001b[36m28\u001b[0m \u001b[32mThe\u001b[0m \u001b[33moverall\u001b[0m \u001b[31mreduced\u001b[0m \u001b[31mrequirements\u001b[0m \u001b[35mwere\u001b[0m \u001b[35moffset\u001b[0m \u001b[35min\u001b[0m \u001b[35mpart\u001b[0m \u001b[35mby\u001b[0m \u001b[36madditional\u001b[0m \u001b[37mrequirements\u001b[0m \u001b[37mwith\u001b[0m \u001b[37mrespect\u001b[0m \u001b[37mto\u001b[0m \u001b[37mcivilian\u001b[0m \u001b[37mpersonnel\u001b[0m \u001b[37mcosts\u001b[0m \u001b[37mdue\u001b[0m \u001b[37mmainly\u001b[0m \u001b[37mto\u001b[0m \u001b[37mhigher\u001b[0m \u001b[37mactual\u001b[0m \u001b[37mdeployment\u001b[0m \u001b[37mof\u001b[0m \u001b[37minternational\u001b[0m \u001b[37mstaff\u001b[0m \u001b[37man\u001b[0m \u001b[37mactual\u001b[0m \u001b[37maverage\u001b[0m \u001b[37mstrength\u001b[0m \u001b[37mof\u001b[0m \u001b[37m316\u001b[0m \u001b[37mcompared\u001b[0m \u001b[37mwith\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mprojected\u001b[0m \u001b[37maverage\u001b[0m \u001b[37mstrength\u001b[0m \u001b[37mof\u001b[0m \u001b[37m280\u001b[0m\n",
      "sample:  \u001b[36m28\u001b[0m \u001b[36m28\u001b[0m \u001b[32mdepts\u001b[0m \u001b[32m杂项收入\u001b[0m \u001b[33m整体\u001b[0m \u001b[33m支出\u001b[0m \u001b[33m总额\u001b[0m \u001b[31m的\u001b[0m \u001b[35m调整\u001b[0m\n",
      "source:  28 The overall reduced requirements were offset in part by additional requirements with respect to civilian personnel costs due mainly to higher actual deployment of international staff an actual average strength of 316 compared with the projected average strength of 280\n",
      "sample:  28 28 depts 杂项收入 整体 支出 总额 的 调整\n",
      "target:  28 文职人员 所 需 资源 增加 主要 原因 是 实际 部署 的 国际 工作人员 增多 实际 平均 人数 为 316 人 而 预计 平均 人数 为 280 人 这部分 抵消 了 所 需 经费 减少 总额\n",
      "quality: 0.000265020043788\n",
      "delay: 0.372727269339\n",
      "reward: 0.000530040087575\n",
      "--Id: 26\n",
      "source:  \u001b[36mDirec@@\u001b[0m \u001b[32mting\u001b[0m \u001b[32mattacks\u001b[0m \u001b[32magainst\u001b[0m \u001b[33mpersonnel\u001b[0m \u001b[33minstallations\u001b[0m \u001b[33mmaterial\u001b[0m \u001b[33munits\u001b[0m \u001b[31mor\u001b[0m \u001b[35mvehicles\u001b[0m \u001b[35minvolved\u001b[0m \u001b[36min\u001b[0m \u001b[36ma\u001b[0m \u001b[37mhumanitarian\u001b[0m \u001b[37massistance\u001b[0m \u001b[37mor\u001b[0m \u001b[37mpeacekeeping\u001b[0m \u001b[37mmission\u001b[0m \u001b[37min\u001b[0m \u001b[37maccordance\u001b[0m \u001b[37mwith\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mUnited\u001b[0m \u001b[37mNations\u001b[0m \u001b[37mCharter\u001b[0m \u001b[37mas\u001b[0m \u001b[37mlong\u001b[0m \u001b[37mas\u001b[0m \u001b[37mthey\u001b[0m \u001b[37mare\u001b[0m \u001b[37mentitled\u001b[0m \u001b[37mto\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mprotection\u001b[0m \u001b[37mgiven\u001b[0m \u001b[37mto\u001b[0m \u001b[37mcivilians\u001b[0m \u001b[37mor\u001b[0m \u001b[37mcivilian\u001b[0m \u001b[37mobjects\u001b[0m \u001b[37munder\u001b[0m \u001b[37minternational\u001b[0m \u001b[37mhumanitarian\u001b[0m \u001b[37mlaw\u001b[0m\n",
      "sample:  \u001b[36m方向\u001b[0m \u001b[32m同\u001b[0m \u001b[32m袭击\u001b[0m \u001b[33m为\u001b[0m \u001b[31m手法\u001b[0m \u001b[31mAP@@\u001b[0m \u001b[35ms\u001b[0m\n",
      "source:  Direc@@ ting attacks against personnel installations material units or vehicles involved in a humanitarian assistance or peacekeeping mission in accordance with the United Nations Charter as long as they are entitled to the protection given to civilians or civilian objects under international humanitarian law\n",
      "sample:  方向 同 袭击 为 手法 AP@@ s\n",
      "target:  UNK 攻击 执行 人道主义 援助 行动 的 人员 设施 物资 单位 或 车辆 或 根据 联合国 宪章 设立 的 维持 和平 特派团 只要 后者 根据 国际 人道主义法 有权 享有 相同 于 平民 或 民用 物体 的 保护\n",
      "quality: 0.0\n",
      "delay: 0.567307686853\n",
      "reward: 0.0\n",
      "--Id: 27\n",
      "source:  \u001b[36mHowever\u001b[0m \u001b[32mthe\u001b[0m \u001b[33mincrease\u001b[0m \u001b[31mwas\u001b[0m \u001b[31moffset\u001b[0m \u001b[31mto\u001b[0m \u001b[31msome\u001b[0m \u001b[31mextent\u001b[0m \u001b[31mby\u001b[0m \u001b[35mprice\u001b[0m \u001b[36mdeclines\u001b[0m \u001b[32min\u001b[0m \u001b[32mmanufactured\u001b[0m \u001b[37mgoods\u001b[0m \u001b[37mso\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mnotwithstanding\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mimprovement\u001b[0m \u001b[37min\u001b[0m \u001b[37msome\u001b[0m \u001b[37mcommodity\u001b[0m \u001b[37mprices\u001b[0m \u001b[37mon\u001b[0m \u001b[37man\u001b[0m \u001b[37moverall\u001b[0m \u001b[37mbasis\u001b[0m \u001b[37mdeveloping\u001b[0m \u001b[37meconomies\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mESCAP\u001b[0m \u001b[37mregion\u001b[0m \u001b[37msuffered\u001b[0m \u001b[37mter@@\u001b[0m \u001b[37mm@@\u001b[0m \u001b[37ms@@\u001b[0m \u001b[37mof@@\u001b[0m \u001b[37mtrade\u001b[0m \u001b[37mlosses\u001b[0m \u001b[37mduring\u001b[0m \u001b[37mthe\u001b[0m \u001b[37myear\u001b[0m\n",
      "sample:  \u001b[36m但是\u001b[0m \u001b[32m然而\u001b[0m \u001b[33m增加\u001b[0m \u001b[31m与\u001b[0m \u001b[35m因果关系\u001b[0m \u001b[36m至少\u001b[0m \u001b[36m出现\u001b[0m \u001b[36m了\u001b[0m\n",
      "source:  However the increase was offset to some extent by price declines in manufactured goods so that notwithstanding the improvement in some commodity prices on an overall basis developing economies of the ESCAP region suffered ter@@ m@@ s@@ of@@ trade losses during the year\n",
      "sample:  但是 然而 增加 与 因果关系 至少 出现 了\n",
      "target:  但是 这一 增长 在 一定 程度 上 受到 了 制成品 价格 下跌 的 抵消 因此 尽管 某些 商品价格 出现 了 一些 起色 但 总的来说 亚太经社会 的 发展 中 经济体 在 贸易条件 方面 受到 了 损失\n",
      "quality: 0.000165693344378\n",
      "delay: 0.606837601651\n",
      "reward: 0.000331386688757\n",
      "--Id: 28\n",
      "source:  \u001b[36mb\u001b[0m \u001b[32mWhen\u001b[0m \u001b[33mthe\u001b[0m \u001b[31mwork\u001b[0m \u001b[31mof\u001b[0m \u001b[35mthe\u001b[0m \u001b[35mSixth\u001b[0m \u001b[35mCommittee\u001b[0m \u001b[35mcontains\u001b[0m \u001b[35mnew\u001b[0m \u001b[35mtopics\u001b[0m \u001b[36mor\u001b[0m \u001b[36mtopics\u001b[0m \u001b[36mrarely\u001b[0m \u001b[36mcovered\u001b[0m \u001b[37mby\u001b[0m \u001b[37mUnited\u001b[0m \u001b[37mNations\u001b[0m \u001b[37mbodies\u001b[0m \u001b[37mOL@@\u001b[0m \u001b[37mA\u001b[0m \u001b[37mshould\u001b[0m \u001b[37moffer\u001b[0m \u001b[37mto\u001b[0m \u001b[37mprepare\u001b[0m \u001b[37mbackground\u001b[0m \u001b[37mdocuments\u001b[0m \u001b[37mon\u001b[0m \u001b[37mlegal\u001b[0m \u001b[37maspects\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mtopics\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mwould\u001b[0m \u001b[37mfacilitate\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mconsideration\u001b[0m \u001b[37mby\u001b[0m \u001b[37mdelegations\u001b[0m \u001b[37min\u001b[0m \u001b[37msubsequent\u001b[0m \u001b[37msessions\u001b[0m\n",
      "sample:  \u001b[36mb\u001b[0m \u001b[32m水\u001b[0m \u001b[33m量\u001b[0m \u001b[33m第二次\u001b[0m \u001b[31m时\u001b[0m \u001b[31m养育\u001b[0m \u001b[35m至\u001b[0m\n",
      "source:  b When the work of the Sixth Committee contains new topics or topics rarely covered by United Nations bodies OL@@ A should offer to prepare background documents on legal aspects of the topics that would facilitate their consideration by delegations in subsequent sessions\n",
      "sample:  b 水 量 第二次 时 养育 至\n",
      "target:  b 当 第六 委员会 的 工作 涉及 新 议题 或 联合国 机构 很少 审议 的 议题 时 法律 厅应 主动 编制 有关 这些 议题 涉及 的 法律 问题 的 背景 文件 以便 于 各国 代表团 在 今后 届会 中 对 其 进行 审议\n",
      "quality: 3.43924054195e-06\n",
      "delay: 0.374999996875\n",
      "reward: 6.8784810839e-06\n",
      "--Id: 29\n",
      "source:  \u001b[36mHe\u001b[0m \u001b[36mnoted\u001b[0m \u001b[36mthat\u001b[0m \u001b[36mmost\u001b[0m \u001b[32mof\u001b[0m \u001b[32mthe\u001b[0m \u001b[33mparticipants\u001b[0m \u001b[33min\u001b[0m \u001b[33mthe\u001b[0m \u001b[31msymposium\u001b[0m \u001b[31mwere\u001b[0m \u001b[35mof\u001b[0m \u001b[36mthe\u001b[0m \u001b[37mview\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mhost\u001b[0m \u001b[37mcountries\u001b[0m \u001b[37mshould\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mable\u001b[0m \u001b[37mto\u001b[0m \u001b[37mregulate\u001b[0m \u001b[37mFDI\u001b[0m \u001b[37mso\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mthey\u001b[0m \u001b[37mcould\u001b[0m \u001b[37mchoose\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mown\u001b[0m \u001b[37mparticular\u001b[0m \u001b[37mmix\u001b[0m \u001b[37mof\u001b[0m \u001b[37mpolicies\u001b[0m \u001b[37mand\u001b[0m \u001b[37mconditions\u001b[0m \u001b[37mrelating\u001b[0m \u001b[37mto\u001b[0m \u001b[37mFDI\u001b[0m \u001b[37mkeeping\u001b[0m \u001b[37min\u001b[0m \u001b[37mmind\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mdevelopmental\u001b[0m \u001b[37mneeds\u001b[0m\n",
      "sample:  \u001b[36m他\u001b[0m \u001b[36m指出\u001b[0m \u001b[36m大多数\u001b[0m \u001b[36m人\u001b[0m \u001b[32m多数\u001b[0m \u001b[33m参与\u001b[0m \u001b[31m了\u001b[0m \u001b[35m专题讨论\u001b[0m \u001b[36m会\u001b[0m\n",
      "source:  He noted that most of the participants in the symposium were of the view that host countries should be able to regulate FDI so that they could choose their own particular mix of policies and conditions relating to FDI keeping in mind their developmental needs\n",
      "sample:  他 指出 大多数 人 多数 参与 了 专题讨论 会\n",
      "target:  他 指出 大多数 与会者 认为 接受 投资 的 国家 应该 能够 管理 外国 直接 投资 以便 能 结合 自己 的 发展 需要 选择 有 自己 特点 的 外国 直接 投资 政策 和 条件 的 组合\n",
      "quality: 0.000648685642831\n",
      "delay: 0.615384610651\n",
      "reward: 0.00129737128566\n",
      "--Id: 30\n",
      "source:  \u001b[36mb\u001b[0m \u001b[36mWhen\u001b[0m \u001b[32mthe\u001b[0m \u001b[33mwork\u001b[0m \u001b[33mof\u001b[0m \u001b[31mthe\u001b[0m \u001b[31mSixth\u001b[0m \u001b[35mCommittee\u001b[0m \u001b[36mcontains\u001b[0m \u001b[37mnew\u001b[0m \u001b[37mtopics\u001b[0m \u001b[37mor\u001b[0m \u001b[37mtopics\u001b[0m \u001b[37mrarely\u001b[0m \u001b[37mcovered\u001b[0m \u001b[37mby\u001b[0m \u001b[37mUnited\u001b[0m \u001b[37mNations\u001b[0m \u001b[37mbodies\u001b[0m \u001b[37mOL@@\u001b[0m \u001b[37mA\u001b[0m \u001b[37mshould\u001b[0m \u001b[37moffer\u001b[0m \u001b[37mto\u001b[0m \u001b[37mprepare\u001b[0m \u001b[37mbackground\u001b[0m \u001b[37mdocuments\u001b[0m \u001b[37mon\u001b[0m \u001b[37mlegal\u001b[0m \u001b[37maspects\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mtopics\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mwould\u001b[0m \u001b[37mfacilitate\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mconsideration\u001b[0m \u001b[37mby\u001b[0m \u001b[37mdelegations\u001b[0m \u001b[37min\u001b[0m \u001b[37msubsequent\u001b[0m \u001b[37msessions\u001b[0m\n",
      "sample:  \u001b[36mb\u001b[0m \u001b[36m何时\u001b[0m \u001b[36m发生\u001b[0m \u001b[32m什么\u001b[0m \u001b[32m日期\u001b[0m \u001b[33m的\u001b[0m \u001b[33m工作\u001b[0m \u001b[33m当@@\u001b[0m \u001b[33m工\u001b[0m \u001b[33m时\u001b[0m \u001b[31m第\u001b[0m \u001b[31m6\u001b[0m \u001b[35m种\u001b[0m \u001b[36m规定\u001b[0m\n",
      "source:  b When the work of the Sixth Committee contains new topics or topics rarely covered by United Nations bodies OL@@ A should offer to prepare background documents on legal aspects of the topics that would facilitate their consideration by delegations in subsequent sessions\n",
      "sample:  b 何时 发生 什么 日期 的 工作 当@@ 工 时 第 6 种 规定\n",
      "target:  b 当 第六 委员会 的 工作 涉及 新 议题 或 联合国 机构 很少 审议 的 议题 时 法律 厅应 主动 编制 有关 这些 议题 涉及 的 法律 问题 的 背景 文件 以便 于 各国 代表团 在 今后 届会 中 对 其 进行 审议\n",
      "quality: 0.0011956466482\n",
      "delay: 0.570370366145\n",
      "reward: 0.0023912932964\n",
      "--Id: 31\n",
      "source:  \u001b[36mb\u001b[0m \u001b[36mWhen\u001b[0m \u001b[36mthe\u001b[0m \u001b[36mwork\u001b[0m \u001b[32mof\u001b[0m \u001b[32mthe\u001b[0m \u001b[32mSixth\u001b[0m \u001b[32mCommittee\u001b[0m \u001b[33mcontains\u001b[0m \u001b[33mnew\u001b[0m \u001b[33mtopics\u001b[0m \u001b[31mor\u001b[0m \u001b[35mtopics\u001b[0m \u001b[35mrarely\u001b[0m \u001b[37mcovered\u001b[0m \u001b[37mby\u001b[0m \u001b[37mUnited\u001b[0m \u001b[37mNations\u001b[0m \u001b[37mbodies\u001b[0m \u001b[37mOL@@\u001b[0m \u001b[37mA\u001b[0m \u001b[37mshould\u001b[0m \u001b[37moffer\u001b[0m \u001b[37mto\u001b[0m \u001b[37mprepare\u001b[0m \u001b[37mbackground\u001b[0m \u001b[37mdocuments\u001b[0m \u001b[37mon\u001b[0m \u001b[37mlegal\u001b[0m \u001b[37maspects\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mtopics\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mwould\u001b[0m \u001b[37mfacilitate\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mconsideration\u001b[0m \u001b[37mby\u001b[0m \u001b[37mdelegations\u001b[0m \u001b[37min\u001b[0m \u001b[37msubsequent\u001b[0m \u001b[37msessions\u001b[0m\n",
      "sample:  \u001b[36mb\u001b[0m \u001b[36m当\u001b[0m \u001b[36m工\u001b[0m \u001b[32m作\u001b[0m \u001b[33m问题\u001b[0m \u001b[33m时\u001b[0m \u001b[33m由\u001b[0m \u001b[31m问题\u001b[0m \u001b[31m未\u001b[0m \u001b[31m采纳\u001b[0m \u001b[31m的\u001b[0m \u001b[35m除外\u001b[0m\n",
      "source:  b When the work of the Sixth Committee contains new topics or topics rarely covered by United Nations bodies OL@@ A should offer to prepare background documents on legal aspects of the topics that would facilitate their consideration by delegations in subsequent sessions\n",
      "sample:  b 当 工 作 问题 时 由 问题 未 采纳 的 除外\n",
      "target:  b 当 第六 委员会 的 工作 涉及 新 议题 或 联合国 机构 很少 审议 的 议题 时 法律 厅应 主动 编制 有关 这些 议题 涉及 的 法律 问题 的 背景 文件 以便 于 各国 代表团 在 今后 届会 中 对 其 进行 审议\n",
      "quality: 0.000777633634171\n",
      "delay: 0.708791204897\n",
      "reward: 0.00155526726834\n",
      "--Id: 32\n",
      "source:  \u001b[36mHe\u001b[0m \u001b[36mnoted\u001b[0m \u001b[32mthat\u001b[0m \u001b[33mmost\u001b[0m \u001b[31mof\u001b[0m \u001b[35mthe\u001b[0m \u001b[35mparticipants\u001b[0m \u001b[36min\u001b[0m \u001b[32mthe\u001b[0m \u001b[32msymposium\u001b[0m \u001b[32mwere\u001b[0m \u001b[33mof\u001b[0m \u001b[33mthe\u001b[0m \u001b[37mview\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mhost\u001b[0m \u001b[37mcountries\u001b[0m \u001b[37mshould\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mable\u001b[0m \u001b[37mto\u001b[0m \u001b[37mregulate\u001b[0m \u001b[37mFDI\u001b[0m \u001b[37mso\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mthey\u001b[0m \u001b[37mcould\u001b[0m \u001b[37mchoose\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mown\u001b[0m \u001b[37mparticular\u001b[0m \u001b[37mmix\u001b[0m \u001b[37mof\u001b[0m \u001b[37mpolicies\u001b[0m \u001b[37mand\u001b[0m \u001b[37mconditions\u001b[0m \u001b[37mrelating\u001b[0m \u001b[37mto\u001b[0m \u001b[37mFDI\u001b[0m \u001b[37mkeeping\u001b[0m \u001b[37min\u001b[0m \u001b[37mmind\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mdevelopmental\u001b[0m \u001b[37mneeds\u001b[0m\n",
      "sample:  \u001b[36m他\u001b[0m \u001b[36m指出\u001b[0m \u001b[36m了\u001b[0m \u001b[36m他\u001b[0m \u001b[36m指出\u001b[0m \u001b[36m特别\u001b[0m \u001b[32m报告员\u001b[0m \u001b[33m强调指出\u001b[0m \u001b[31m大多数\u001b[0m \u001b[31m难民\u001b[0m \u001b[35m参加\u001b[0m \u001b[36m了\u001b[0m \u001b[32m研讨会\u001b[0m\n",
      "source:  He noted that most of the participants in the symposium were of the view that host countries should be able to regulate FDI so that they could choose their own particular mix of policies and conditions relating to FDI keeping in mind their developmental needs\n",
      "sample:  他 指出 了 他 指出 特别 报告员 强调指出 大多数 难民 参加 了 研讨会\n",
      "target:  他 指出 大多数 与会者 认为 接受 投资 的 国家 应该 能够 管理 外国 直接 投资 以便 能 结合 自己 的 发展 需要 选择 有 自己 特点 的 外国 直接 投资 政策 和 条件 的 组合\n",
      "quality: 0.0037585926841\n",
      "delay: 0.373626371573\n",
      "reward: 0.00751718536821\n",
      "--Id: 33\n",
      "source:  \u001b[36mThe\u001b[0m \u001b[36mSpecial\u001b[0m \u001b[36mRepresentative\u001b[0m \u001b[36mnoted\u001b[0m \u001b[36mthat\u001b[0m \u001b[32mthe\u001b[0m \u001b[33muse\u001b[0m \u001b[33mof\u001b[0m \u001b[33mcriminal\u001b[0m \u001b[33mcharges\u001b[0m \u001b[31msuch\u001b[0m \u001b[37mas\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mencouraging\u001b[0m \u001b[37mhatred\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mState\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mand\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mdistributing\u001b[0m \u001b[37mfal@@\u001b[0m \u001b[37mse@@\u001b[0m \u001b[37mhoods\u001b[0m \u001b[37mand\u001b[0m \u001b[37mr@@\u001b[0m \u001b[37mum@@\u001b[0m \u001b[37mors\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mfrequently\u001b[0m \u001b[37mimplies\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mrisk\u001b[0m \u001b[37mof\u001b[0m \u001b[37msup@@\u001b[0m \u001b[37mpressing\u001b[0m \u001b[37mlegitimate\u001b[0m \u001b[37mfree\u001b[0m \u001b[37mspeech\u001b[0m \u001b[37mand\u001b[0m \u001b[37mis\u001b[0m \u001b[37mparticularly\u001b[0m \u001b[37mworrying\u001b[0m \u001b[37mwhen\u001b[0m \u001b[37msuch\u001b[0m \u001b[37mcharges\u001b[0m \u001b[37mare\u001b[0m \u001b[37mraised\u001b[0m \u001b[37magainst\u001b[0m \u001b[37ma\u001b[0m \u001b[37mperson\u001b[0m \u001b[37mfor\u001b[0m \u001b[37mhaving\u001b[0m \u001b[37mden@@\u001b[0m \u001b[37mounced\u001b[0m \u001b[37malleged\u001b[0m \u001b[37mhuman\u001b[0m \u001b[37mrights\u001b[0m \u001b[37mviolations\u001b[0m\n",
      "sample:  \u001b[36m特别\u001b[0m \u001b[36m代表\u001b[0m \u001b[36m也\u001b[0m \u001b[36m指出\u001b[0m \u001b[36m特别\u001b[0m \u001b[36m代表\u001b[0m \u001b[36m注意\u001b[0m \u001b[32m到\u001b[0m \u001b[32m特别\u001b[0m \u001b[33m代表\u001b[0m \u001b[33m对\u001b[0m \u001b[33m刑事诉讼法\u001b[0m \u001b[33m的\u001b[0m \u001b[33m援用\u001b[0m \u001b[33m引发\u001b[0m \u001b[33m了\u001b[0m \u001b[33m酷刑\u001b[0m\n",
      "source:  The Special Representative noted that the use of criminal charges such as UNK encouraging hatred of the State UNK and UNK distributing fal@@ se@@ hoods and r@@ um@@ ors UNK frequently implies the risk of sup@@ pressing legitimate free speech and is particularly worrying when such charges are raised against a person for having den@@ ounced alleged human rights violations\n",
      "sample:  特别 代表 也 指出 特别 代表 注意 到 特别 代表 对 刑事诉讼法 的 援用 引发 了 酷刑\n",
      "target:  35 特别 代表 注意 到 采用 诸如 鼓励 对 国家 的 仇恨 和 散布 谎言 和 UNK 言 等 指控 常常 意味着 压制 合法 自由言论 的 风险 在 针对 谴责 指称 侵犯 人权 事项 者 提出 这种 指控 时 尤其 令人担心\n",
      "quality: 0.013725493175\n",
      "delay: 0.69696969345\n",
      "reward: 0.02745098635\n",
      "--Id: 34\n",
      "source:  \u001b[36m178\u001b[0m \u001b[32mThe\u001b[0m \u001b[32mWorking\u001b[0m \u001b[32mGroup\u001b[0m \u001b[33mwishes\u001b[0m \u001b[33mto\u001b[0m \u001b[31mremind\u001b[0m \u001b[31mthe\u001b[0m \u001b[31mGovernment\u001b[0m \u001b[35mof\u001b[0m \u001b[36mthe\u001b[0m \u001b[36mRepublic\u001b[0m \u001b[36mof\u001b[0m \u001b[32mthe\u001b[0m \u001b[32mCongo\u001b[0m \u001b[33mof\u001b[0m \u001b[37mits\u001b[0m \u001b[37mresponsibility\u001b[0m \u001b[37mto\u001b[0m \u001b[37mconduct\u001b[0m \u001b[37mthorough\u001b[0m \u001b[37mand\u001b[0m \u001b[37mimpartial\u001b[0m \u001b[37minvestigations\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mfor\u001b[0m \u001b[37mas\u001b[0m \u001b[37mlong\u001b[0m \u001b[37mas\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mfate\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mvictim\u001b[0m \u001b[37mof\u001b[0m \u001b[37menforced\u001b[0m \u001b[37mdisappearance\u001b[0m \u001b[37mremains\u001b[0m \u001b[37mun@@\u001b[0m \u001b[37mclarified\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37min\u001b[0m \u001b[37maccordance\u001b[0m \u001b[37mwith\u001b[0m \u001b[37marticle\u001b[0m \u001b[37m13\u001b[0m \u001b[37mparagraph\u001b[0m \u001b[37m6\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mDeclaration\u001b[0m\n",
      "sample:  \u001b[36m178\u001b[0m \u001b[32m甘@@\u001b[0m \u001b[32m蔗\u001b[0m \u001b[33m收入\u001b[0m \u001b[31m和\u001b[0m \u001b[35m储存\u001b[0m \u001b[35m木@@\u001b[0m \u001b[35m新@@\u001b[0m \u001b[35m港\u001b[0m \u001b[36m未@@\u001b[0m \u001b[32m果\u001b[0m \u001b[33m政府\u001b[0m\n",
      "source:  178 The Working Group wishes to remind the Government of the Republic of the Congo of its responsibility to conduct thorough and impartial investigations UNK for as long as the fate of the victim of enforced disappearance remains un@@ clarified UNK in accordance with article 13 paragraph 6 of the Declaration\n",
      "sample:  178 甘@@ 蔗 收入 和 储存 木@@ 新@@ 港 未@@ 果 政府\n",
      "target:  178 工作组 谨 提请 刚果共和国 政府 注意 其所 承担 的 责任 即 按照 宣言 第 13 条 第 6 款 进行 彻底 和 公正 的 调查 直至 查明 被 强迫 失踪 的 人 的 命运 为止\n",
      "quality: 0.000102370196888\n",
      "delay: 0.596153843288\n",
      "reward: 0.000204740393776\n",
      "--Id: 35\n",
      "source:  \u001b[36m178\u001b[0m \u001b[36mThe\u001b[0m \u001b[32mWorking\u001b[0m \u001b[33mGroup\u001b[0m \u001b[33mwishes\u001b[0m \u001b[33mto\u001b[0m \u001b[31mremind\u001b[0m \u001b[31mthe\u001b[0m \u001b[35mGovernment\u001b[0m \u001b[36mof\u001b[0m \u001b[36mthe\u001b[0m \u001b[36mRepublic\u001b[0m \u001b[32mof\u001b[0m \u001b[33mthe\u001b[0m \u001b[31mCongo\u001b[0m \u001b[37mof\u001b[0m \u001b[37mits\u001b[0m \u001b[37mresponsibility\u001b[0m \u001b[37mto\u001b[0m \u001b[37mconduct\u001b[0m \u001b[37mthorough\u001b[0m \u001b[37mand\u001b[0m \u001b[37mimpartial\u001b[0m \u001b[37minvestigations\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mfor\u001b[0m \u001b[37mas\u001b[0m \u001b[37mlong\u001b[0m \u001b[37mas\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mfate\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mvictim\u001b[0m \u001b[37mof\u001b[0m \u001b[37menforced\u001b[0m \u001b[37mdisappearance\u001b[0m \u001b[37mremains\u001b[0m \u001b[37mun@@\u001b[0m \u001b[37mclarified\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37min\u001b[0m \u001b[37maccordance\u001b[0m \u001b[37mwith\u001b[0m \u001b[37marticle\u001b[0m \u001b[37m13\u001b[0m \u001b[37mparagraph\u001b[0m \u001b[37m6\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mDeclaration\u001b[0m\n",
      "sample:  \u001b[36m178\u001b[0m \u001b[32m工作\u001b[0m \u001b[32m方面\u001b[0m \u001b[32m作\u001b[0m \u001b[33m了\u001b[0m \u001b[31m下列\u001b[0m \u001b[35m回复\u001b[0m \u001b[36m并\u001b[0m \u001b[32m提醒\u001b[0m \u001b[32m该国\u001b[0m \u001b[32m政府\u001b[0m \u001b[33m注意\u001b[0m \u001b[33m阿拉伯叙利亚共和国\u001b[0m \u001b[33m政府\u001b[0m\n",
      "source:  178 The Working Group wishes to remind the Government of the Republic of the Congo of its responsibility to conduct thorough and impartial investigations UNK for as long as the fate of the victim of enforced disappearance remains un@@ clarified UNK in accordance with article 13 paragraph 6 of the Declaration\n",
      "sample:  178 工作 方面 作 了 下列 回复 并 提醒 该国 政府 注意 阿拉伯叙利亚共和国 政府\n",
      "target:  178 工作组 谨 提请 刚果共和国 政府 注意 其所 承担 的 责任 即 按照 宣言 第 13 条 第 6 款 进行 彻底 和 公正 的 调查 直至 查明 被 强迫 失踪 的 人 的 命运 为止\n",
      "quality: 0.00463877078523\n",
      "delay: 0.631111108306\n",
      "reward: 0.00927754157046\n",
      "--Id: 36\n",
      "source:  \u001b[36mThe\u001b[0m \u001b[36mState\u001b[0m \u001b[36mparty\u001b[0m \u001b[32mshould\u001b[0m \u001b[32mpromulg@@\u001b[0m \u001b[33mate\u001b[0m \u001b[33mlegal\u001b[0m \u001b[33mprovisions\u001b[0m \u001b[33mthat\u001b[0m \u001b[33mrecognize\u001b[0m \u001b[31mthe\u001b[0m \u001b[35mright\u001b[0m \u001b[36mto\u001b[0m \u001b[36mconscientious\u001b[0m \u001b[32mobjection\u001b[0m \u001b[32mto\u001b[0m \u001b[32mmilitary\u001b[0m \u001b[37mservice\u001b[0m \u001b[37mand\u001b[0m \u001b[37mestablish\u001b[0m \u001b[37man\u001b[0m \u001b[37malternative\u001b[0m \u001b[37mto\u001b[0m \u001b[37mmilitary\u001b[0m \u001b[37mservice\u001b[0m \u001b[37mthat\u001b[0m \u001b[37mis\u001b[0m \u001b[37maccessible\u001b[0m \u001b[37mto\u001b[0m \u001b[37mall\u001b[0m \u001b[37mconscientious\u001b[0m \u001b[37mobjec@@\u001b[0m \u001b[37mtors\u001b[0m \u001b[37mand\u001b[0m \u001b[37mis\u001b[0m \u001b[37mnot\u001b[0m \u001b[37mpunitive\u001b[0m \u001b[37mor\u001b[0m \u001b[37mdiscriminatory\u001b[0m \u001b[37min\u001b[0m \u001b[37mterms\u001b[0m \u001b[37mof\u001b[0m \u001b[37mits\u001b[0m \u001b[37mnature\u001b[0m \u001b[37mcost\u001b[0m \u001b[37mor\u001b[0m \u001b[37mduration\u001b[0m\n",
      "sample:  \u001b[36m缔约国\u001b[0m \u001b[36m重申\u001b[0m \u001b[32m该\u001b[0m \u001b[33m立法\u001b[0m \u001b[33m条文\u001b[0m \u001b[31m应当\u001b[0m \u001b[31m认明\u001b[0m \u001b[31m承认\u001b[0m \u001b[35m权利\u001b[0m \u001b[36m的\u001b[0m \u001b[36m法律\u001b[0m \u001b[36m规定\u001b[0m\n",
      "source:  The State party should promulg@@ ate legal provisions that recognize the right to conscientious objection to military service and establish an alternative to military service that is accessible to all conscientious objec@@ tors and is not punitive or discriminatory in terms of its nature cost or duration\n",
      "sample:  缔约国 重申 该 立法 条文 应当 认明 承认 权利 的 法律 规定\n",
      "target:  缔约国 应 颁布 法规 承认 出于 良心 拒 服兵役 的 权利 并 制订 兵役 替代 措施 使 所有 出于 良心 拒 服兵役 者 都 可 享有 并且 在 性质 费用 或 持续时间 上 不 具有 惩罚性 或 歧视性\n",
      "quality: 0.00140314598501\n",
      "delay: 0.610859725743\n",
      "reward: 0.00280629197002\n",
      "--Id: 37\n",
      "source:  \u001b[36m178\u001b[0m \u001b[36mThe\u001b[0m \u001b[36mWorking\u001b[0m \u001b[32mGroup\u001b[0m \u001b[33mwishes\u001b[0m \u001b[33mto\u001b[0m \u001b[33mremind\u001b[0m \u001b[31mthe\u001b[0m \u001b[31mGovernment\u001b[0m \u001b[31mof\u001b[0m \u001b[35mthe\u001b[0m \u001b[35mRepublic\u001b[0m \u001b[36mof\u001b[0m \u001b[32mthe\u001b[0m \u001b[32mCongo\u001b[0m \u001b[33mof\u001b[0m \u001b[33mits\u001b[0m \u001b[37mresponsibility\u001b[0m \u001b[37mto\u001b[0m \u001b[37mconduct\u001b[0m \u001b[37mthorough\u001b[0m \u001b[37mand\u001b[0m \u001b[37mimpartial\u001b[0m \u001b[37minvestigations\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mfor\u001b[0m \u001b[37mas\u001b[0m \u001b[37mlong\u001b[0m \u001b[37mas\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mfate\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mvictim\u001b[0m \u001b[37mof\u001b[0m \u001b[37menforced\u001b[0m \u001b[37mdisappearance\u001b[0m \u001b[37mremains\u001b[0m \u001b[37mun@@\u001b[0m \u001b[37mclarified\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37min\u001b[0m \u001b[37maccordance\u001b[0m \u001b[37mwith\u001b[0m \u001b[37marticle\u001b[0m \u001b[37m13\u001b[0m \u001b[37mparagraph\u001b[0m \u001b[37m6\u001b[0m \u001b[37mof\u001b[0m \u001b[37mthe\u001b[0m \u001b[37mDeclaration\u001b[0m\n",
      "sample:  \u001b[36m178\u001b[0m \u001b[32m工作组\u001b[0m \u001b[33m愿\u001b[0m \u001b[33m提醒\u001b[0m \u001b[31m该国\u001b[0m \u001b[31m政府\u001b[0m \u001b[31m提醒\u001b[0m \u001b[35m其\u001b[0m \u001b[36m政府\u001b[0m \u001b[36m警惕\u001b[0m \u001b[32m刚果共和国\u001b[0m \u001b[33m政府\u001b[0m\n",
      "source:  178 The Working Group wishes to remind the Government of the Republic of the Congo of its responsibility to conduct thorough and impartial investigations UNK for as long as the fate of the victim of enforced disappearance remains un@@ clarified UNK in accordance with article 13 paragraph 6 of the Declaration\n",
      "sample:  178 工作组 愿 提醒 该国 政府 提醒 其 政府 警惕 刚果共和国 政府\n",
      "target:  178 工作组 谨 提请 刚果共和国 政府 注意 其所 承担 的 责任 即 按照 宣言 第 13 条 第 6 款 进行 彻底 和 公正 的 调查 直至 查明 被 强迫 失踪 的 人 的 命运 为止\n",
      "quality: 0.00263668211264\n",
      "delay: 0.624434386315\n",
      "reward: 0.00527336422528\n",
      "--Id: 38\n",
      "source:  \u001b[36mIn\u001b[0m \u001b[36minformation\u001b[0m \u001b[36mprovided\u001b[0m \u001b[32mon\u001b[0m \u001b[33mthe\u001b[0m \u001b[31mapplication\u001b[0m \u001b[31mof\u001b[0m \u001b[31mthe\u001b[0m \u001b[31mCartagena\u001b[0m \u001b[31mAction\u001b[0m \u001b[35mPlan\u001b[0m \u001b[35mon\u001b[0m \u001b[35m13\u001b[0m \u001b[36mSeptember\u001b[0m \u001b[32m2010\u001b[0m \u001b[37mEthiopia\u001b[0m \u001b[37mreported\u001b[0m \u001b[37m13\u001b[0m \u001b[37mareas\u001b[0m \u001b[37min\u001b[0m \u001b[37mwhich\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mmines\u001b[0m \u001b[37mare\u001b[0m \u001b[37mknown\u001b[0m \u001b[37mto\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mem@@\u001b[0m \u001b[37mplaced\u001b[0m \u001b[37mand\u001b[0m \u001b[37m44\u001b[0m \u001b[37mareas\u001b[0m \u001b[37min\u001b[0m \u001b[37mwhich\u001b[0m \u001b[37mUNK\u001b[0m \u001b[37mmines\u001b[0m \u001b[37mare\u001b[0m \u001b[37msuspected\u001b[0m \u001b[37mto\u001b[0m \u001b[37mbe\u001b[0m \u001b[37mem@@\u001b[0m \u001b[37mplaced\u001b[0m\n",
      "sample:  \u001b[36m在\u001b[0m \u001b[36m所\u001b[0m \u001b[36m提供\u001b[0m \u001b[32m的\u001b[0m \u001b[32m信息\u001b[0m \u001b[32m中\u001b[0m \u001b[33m都\u001b[0m \u001b[33m提供\u001b[0m \u001b[31m了\u001b[0m \u001b[35m关于\u001b[0m \u001b[35m卡塔赫纳\u001b[0m \u001b[35m行动计划\u001b[0m \u001b[36m的\u001b[0m \u001b[32m请求\u001b[0m\n",
      "source:  In information provided on the application of the Cartagena Action Plan on 13 September 2010 Ethiopia reported 13 areas in which UNK mines are known to be em@@ placed and 44 areas in which UNK mines are suspected to be em@@ placed\n",
      "sample:  在 所 提供 的 信息 中 都 提供 了 关于 卡塔赫纳 行动计划 的 请求\n",
      "target:  埃塞俄比亚 在 2010 年 9 月 13 日 关于 实施 卡塔赫纳 行动计划 的 资料 中 报告 了 13 个 已知 布设 了 杀伤 人员 地雷 的 区域 和 44 个 怀疑 布设 了 杀伤 人员 地雷 的 区域\n",
      "quality: 0.0065393408572\n",
      "delay: 0.551111108662\n",
      "reward: 0.0130786817144\n",
      "--Id: 39\n",
      "source:  \u001b[36m15\u001b[0m \u001b[32mThere\u001b[0m \u001b[32mhave\u001b[0m \u001b[32mbeen\u001b[0m \u001b[32msuccessful\u001b[0m \u001b[32mexamples\u001b[0m \u001b[32mof\u001b[0m \u001b[32mspecial\u001b[0m \u001b[32mmeasures\u001b[0m \u001b[32mtaken\u001b[0m \u001b[32mto\u001b[0m \u001b[32mmitigate\u001b[0m \u001b[32mthe\u001b[0m \u001b[33mimpact\u001b[0m \u001b[33mof\u001b[0m \u001b[33meconomic\u001b[0m \u001b[31mcrises\u001b[0m \u001b[31mon\u001b[0m \u001b[35mwomen\u001b[0m \u001b[37mand\u001b[0m \u001b[37mgirls\u001b[0m \u001b[37mincluding\u001b[0m \u001b[37mmaintaining\u001b[0m \u001b[37mnecessary\u001b[0m \u001b[37msocial\u001b[0m \u001b[37msector\u001b[0m \u001b[37mexpenditures\u001b[0m \u001b[37mand\u001b[0m \u001b[37mimplementing\u001b[0m \u001b[37msocial\u001b[0m \u001b[37mprotection\u001b[0m \u001b[37mpolicies\u001b[0m \u001b[37mto\u001b[0m \u001b[37mensure\u001b[0m \u001b[37mtheir\u001b[0m \u001b[37mrights\u001b[0m \u001b[37mto\u001b[0m \u001b[37mhealth\u001b[0m \u001b[37mcare\u001b[0m \u001b[37meducation\u001b[0m \u001b[37mand\u001b[0m \u001b[37mmaternal\u001b[0m \u001b[37mhealth\u001b[0m \u001b[37mservices\u001b[0m\n",
      "sample:  \u001b[36m15\u001b[0m \u001b[36m15\u001b[0m \u001b[32m有\u001b[0m \u001b[32m特别\u001b[0m \u001b[32m措施\u001b[0m \u001b[32m减轻\u001b[0m \u001b[33m经济\u001b[0m \u001b[31m危机\u001b[0m \u001b[31m对\u001b[0m \u001b[31m经济\u001b[0m\n",
      "source:  15 There have been successful examples of special measures taken to mitigate the impact of economic crises on women and girls including maintaining necessary social sector expenditures and implementing social protection policies to ensure their rights to health care education and maternal health services\n",
      "sample:  15 15 有 特别 措施 减轻 经济 危机 对 经济\n",
      "target:  15 在 采取 特别 措施 减轻 经济危机 对 妇女 和 女童 的 影响 方面 已有 成功 的 范例 包括 维持 必要 的 社会 部门 支出 和 执行 社会保障 政策 以 确保 她们 享受 保健 教育 和 产妇 保健 服务 的 权利\n",
      "quality: 0.000447588666902\n",
      "delay: 0.684210523042\n",
      "reward: -0.0141048226662\n"
     ]
    }
   ],
   "source": [
    "it = 1\n",
    "\n",
    "# obtain the translation results\n",
    "csamples = _bpe2words(\n",
    "        _seqs2words(statistics['sample'], word_idict_trg,\n",
    "                    statistics['action'], 1))\n",
    "csources =  _bpe2words(\n",
    "        _seqs2words(statistics['SWord'], word_idict,\n",
    "                    statistics['action'], 0))\n",
    "sources  = _seqs2words(statistics['SWord'], word_idict)\n",
    "samples  = _seqs2words(statistics['sample'], word_idict_trg)\n",
    "targets  =  _bpe2words(\n",
    "        _seqs2words(statistics['TWord'], word_idict_trg))\n",
    "\n",
    "c  = 0\n",
    "for j in range(20, 40):\n",
    "    print '--Id: {}'.format(j)\n",
    "    print 'source: ', csources[j]\n",
    "    print 'sample: ', csamples[j]\n",
    "    print 'source: ', sources[j]\n",
    "    print 'sample: ', samples[j]\n",
    "    print 'target: ', targets[j]\n",
    "    print 'quality:', statistics['track'][j][0]\n",
    "    print 'delay:',   statistics['track'][j][1]\n",
    "    print 'reward:',  statistics['track'][j][2]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "b 用以 加强 主席团 / 小组 提出 的 新 议题\n",
      "b 用以 加强 主席团 / 小组 提出 的 新 议题\n"
     ]
    }
   ],
   "source": [
    "def is_str(s):\n",
    "    return isinstance(s, basestring)\n",
    "print is_str(samples[j])\n",
    "print samples[j]\n",
    "print samples[j].decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# reload(matplotlib) \n",
    "\n",
    "# import matplotlib.font_manager as fm\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rc\n",
    "from matplotlib import rcParams\n",
    "\n",
    "# #rc('font',**{'family':'WenQuanYi Micro Hei', 'weight': 'normal'})\n",
    "# #rc('font', size=8)\n",
    "# plot.rcParams['axes.unicode_minus']=False \n",
    "import copy\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "sns.set(context=\"paper\", font=\"monospace\", style='whitegrid')\n",
    "\n",
    "# rc('text', usetex=True)\n",
    "# rc('text.latex',unicode=True)\n",
    "# rc('text.latex',preamble='\\usepackage[utf8]{inputenc}')\n",
    "# rc('text.latex',preamble='\\usepackage[russian]{babel}')\n",
    "# rc('text.latex',preamble='\\usepackage[german]{babel}')\n",
    "# rc('text.latex',preamble='\\usepackage[ngerman]{babel}')\n",
    "myfont = matplotlib.font_manager.FontProperties(\n",
    "    fname='./utils/msyh.ttf') \n",
    "\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False \n",
    "matplotlib.rcParams['ytick.labelsize'] = 11\n",
    "matplotlib.rcParams['xtick.labelsize'] = 11 \n",
    "\n",
    "def heatmap3(sources, refs, trans, actions, idx, atten=None, full_atten=None, savefig=True, name='test', info=None, show=False):\n",
    "    source = ['*'] + [s.strip() for s in sources[idx].decode('utf8').replace('@@', '--').split()] + ['||']\n",
    "    target = ['*'] + [s.strip() for s in trans[idx].decode('utf8').replace('@@', '--').split()] + ['||'] + ['*']\n",
    "    action = actions[idx]\n",
    "   \n",
    "    flag   = 0\n",
    "    if atten:\n",
    "        attention = numpy.array(atten[idx])\n",
    "    else:\n",
    "        attention = None\n",
    "\n",
    "    if full_atten:\n",
    "        fullatten = numpy.array(full_atten[idx])\n",
    "    else:\n",
    "        fullatten = None\n",
    "    \n",
    "    def track(acts, data, annote):\n",
    "        x, y, z = 0, 0, 0\n",
    "        for a in acts:\n",
    "            x += (a == 1)\n",
    "            y += (a == 0)\n",
    "            z += (a == 2)\n",
    "\n",
    "            # data[y + 1, x]   = 1\n",
    "            # data[z, x + 1]   = 1\n",
    "            # annote[y, x] = 'W' if a == 0  else 'C'\n",
    "\n",
    "        return data, annote\n",
    "    # print target\n",
    "    \n",
    "    data       = numpy.zeros((len(source), len(target)))\n",
    "    annote     = numpy.chararray(data.shape, itemsize=8)\n",
    "    annote[:]  = '' \n",
    "    data, annote  = track(action, data, annote)\n",
    "    data[1, 0] = 1\n",
    "    \n",
    "    def draw(data_t, ax, attention=None):\n",
    "        \n",
    "        data   = copy.copy(data_t)\n",
    "        data[1:-1, 1:-1] += attention.T\n",
    "        d  = pd.DataFrame(data=data, columns=target, index=source)\n",
    "        # p  = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "        g  = sns.heatmap(d, mask=(data==0), square=True, cbar=False, \n",
    "                         linewidths=0.1, ax=ax, annot=annote, fmt='s')\n",
    "        g.xaxis.tick_top()\n",
    "   \n",
    "        for tick in ax.get_xticklabels():\n",
    "            tick.set_rotation(60)\n",
    "        for tick in ax.get_yticklabels():\n",
    "            tick.set_rotation(0)\n",
    "        for label in ax.get_xticklabels():\n",
    "            label.set_fontproperties(myfont)\n",
    "        \n",
    "        ax.grid(True)\n",
    "        \n",
    "    if full_atten:\n",
    "        f, [ax1, ax2] = plt.subplots(1, 2, figsize=(22, 11))\n",
    "        f.set_canvas(plot.gcf().canvas)\n",
    "    \n",
    "        draw(data, ax1, attention)\n",
    "        draw(data, ax2, fullatten)\n",
    "    else:\n",
    "        f, ax1 = plt.subplots(1, 1, figsize=(22, 22))\n",
    "        f.set_canvas(plt.gcf().canvas)\n",
    "    \n",
    "        draw(data, ax1, attention)\n",
    "\n",
    "    \n",
    "    if savefig:\n",
    "        if not os.path.exists('.images/M_{}'.format(name)):\n",
    "            os.mkdir('.images/M_{}'.format(name))\n",
    "\n",
    "        filename = 'Idx={}||'.format(info['index'])\n",
    "        for w in info:\n",
    "            if w is not 'index':\n",
    "                filename += '.{}={:.2f}'.format(w, float(info[w]))\n",
    "\n",
    "        # print 'saving...'\n",
    "        plt.savefig('.images/M_{}'.format(name) + '/{}'.format(filename) + '.pdf', dpi=100)\n",
    "    \n",
    "    if show:\n",
    "        plt.show()\n",
    "\n",
    "    # print 'plotting done.'\n",
    "    plt.close()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41,)\n"
     ]
    },
    {
     "data": {
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T04mNjeXChQvs3buXPXv2cPHiRd58802aNGnC4cOHqaqqwsrKCnNzcw4ePMgb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc77ed0a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "src = [sources[j]]\n",
    "L   = len(src[0].split())\n",
    "trs = [samples[j]]\n",
    "act = [statistics['action'][j]]\n",
    "att = [[a[:L] for a in statistics['attentions'][j]]]\n",
    "print att[0][0].shape\n",
    "heatmap3(src, None, trs, act, 0, att, None, name='test', info={'index': 'test'}, show=True, savefig=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9PZ3SpUszceJEatSowZIlS5g+fTre3t5ERUVZPUfe39+iRYswGAy0bt2amTNn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc77ee1ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = 25\n",
    "src = [sources[j]]\n",
    "L   = len(src[0].split())\n",
    "trs = [samples[j]]\n",
    "act = [statistics['action'][j]]\n",
    "att = [[a[:L] for a in statistics['attentions'][j]]]\n",
    "print att[0][0].shape\n",
    "heatmap3(src, None, trs, act, 0, att, None, name='test', info={'index': 'test'}, show=True, savefig=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-   \n",
    "from pylab import *  \n",
    " \n",
    "# t = arange(-5*pi, 5*pi, 0.01)  \n",
    "# y = sin(t)/t  \n",
    "# plt.plot(t, y)  \n",
    "# plt.title(u'这里写的是中文',fontproperties=myfont) #指定字体  \n",
    "# plt.xlabel(u'X坐标',fontproperties=myfont)  \n",
    "# plt.ylabel(u'Y坐标',fontproperties=myfont)  \n",
    "# plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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